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Announced in 2016, Gym is an open-source Python library created to help with the development of reinforcement knowing algorithms. It aimed to standardize how environments are defined in [AI](http://linyijiu.cn:3000) research study, making published research study more quickly reproducible [24] [144] while supplying users with a simple interface for interacting with these environments. In 2022, new developments of Gym have been moved to the library Gymnasium. [145] [146] +
Gym Retro
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[Released](https://youarealways.online) in 2018, Gym Retro is a platform for support knowing (RL) research on computer game [147] utilizing RL algorithms and research study generalization. Prior RL research study focused mainly on enhancing representatives to solve single tasks. Gym Retro provides the capability to generalize between games with comparable concepts however different looks.
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RoboSumo
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Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives initially do not have knowledge of how to even stroll, however are provided the objectives of learning to move and to push the [opposing representative](https://nakshetra.com.np) out of the ring. [148] Through this adversarial learning procedure, the agents discover how to adapt to altering conditions. When an agent is then gotten rid of from this virtual environment and put in a new virtual environment with high winds, the agent braces to remain upright, [recommending](https://paxlook.com) it had discovered how to stabilize in a generalized way. [148] [149] [OpenAI's Igor](https://africasfaces.com) Mordatch argued that competitors in between agents might develop an intelligence "arms race" that could increase an agent's ability to function even outside the [context](http://zerovalueentertainment.com3000) of the competition. [148] +
OpenAI 5
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OpenAI Five is a group of 5 OpenAI-curated bots utilized in the competitive five-on-five computer game Dota 2, that learn to play against human gamers at a high ability level completely through trial-and-error algorithms. Before ending up being a team of 5, the very first public presentation happened at The International 2017, the yearly premiere championship competition for the game, where Dendi, a professional Ukrainian player, lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had [discovered](https://git.hichinatravel.com) by playing against itself for two weeks of actual time, which the learning software application was an action in the direction of creating software application that can manage complicated tasks like a cosmetic surgeon. [152] [153] The system utilizes a type of support knowing, as the bots find out over time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as killing an opponent and taking map goals. [154] [155] [156] +
By June 2018, the ability of the bots broadened to play together as a full team of 5, and they had the ability to defeat groups of [amateur](https://interlinkms.lk) and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 [exhibit matches](https://code.in-planet.net) against expert players, however ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champions of the game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public appearance came later that month, where they played in 42,729 total video games in a four-day open online competition, [winning](https://git.limework.net) 99.4% of those games. [165] +
OpenAI 5's systems in Dota 2's bot gamer reveals the difficulties of [AI](http://175.24.176.2:3000) systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has actually shown making use of deep reinforcement knowing (DRL) agents to attain superhuman proficiency in Dota 2 matches. [166] +
Dactyl
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Developed in 2018, Dactyl uses maker finding out to train a Shadow Hand, a human-like robot hand, to control physical objects. [167] It discovers totally in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. [OpenAI dealt](https://www.grandtribunal.org) with the object orientation issue by utilizing domain randomization, a [simulation](https://baripedia.org) approach which exposes the learner to a range of [experiences](https://videobox.rpz24.ir) rather than trying to fit to truth. The set-up for Dactyl, aside from having [movement tracking](http://repo.jd-mall.cn8048) cameras, likewise has RGB cams to enable the robotic to manipulate an approximate item by seeing it. In 2018, OpenAI revealed that the system was able to control a cube and an octagonal prism. [168] +
In 2019, OpenAI demonstrated that Dactyl might solve a Rubik's Cube. The robotic had the ability to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce intricate physics that is harder to model. OpenAI did this by improving the robustness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation approach of generating progressively harder environments. ADR varies from manual domain randomization by not needing a human to define randomization varieties. [169] +
API
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In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing brand-new [AI](https://kyigit.kyigd.com:3000) models developed by OpenAI" to let designers call on it for "any English language [AI](https://code.cypod.me) task". [170] [171] +
Text generation
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The business has actually popularized generative pretrained transformers (GPT). [172] +
OpenAI's initial GPT model ("GPT-1")
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The initial paper on generative pre-training of a transformer-based language design was composed by Alec Radford and his colleagues, and published in preprint on OpenAI's site on June 11, 2018. [173] It revealed how a generative design of language might obtain world understanding and procedure long-range reliances by pre-training on a diverse corpus with long stretches of contiguous text.
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GPT-2
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Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language model and the successor to OpenAI's original GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only minimal demonstrative variations at first released to the general public. The complete version of GPT-2 was not right away released due to concern about [prospective](https://gitlab.iue.fh-kiel.de) abuse, consisting of applications for composing phony news. [174] Some professionals revealed uncertainty that GPT-2 presented a substantial threat.
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In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to identify "neural phony news". [175] Other researchers, such as Jeremy Howard, warned of "the technology to absolutely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be difficult to filter". [176] In November 2019, OpenAI launched the total version of the GPT-2 language design. [177] Several websites host interactive presentations of different instances of GPT-2 and other transformer designs. [178] [179] [180] +
GPT-2's authors argue unsupervised language models to be general-purpose learners, illustrated by GPT-2 attaining cutting edge precision and perplexity on 7 of 8 zero-shot jobs (i.e. the design was not more trained on any task-specific input-output examples).
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The corpus it was trained on, called WebText, contains slightly 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain issues encoding vocabulary with word tokens by utilizing byte pair encoding. This permits representing any string of characters by encoding both private characters and multiple-character tokens. [181] +
GPT-3
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First explained in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is a without supervision transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the full version of GPT-3 contained 175 billion parameters, [184] two orders of magnitude larger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 designs with as couple of as 125 million parameters were likewise trained). [186] +
OpenAI stated that GPT-3 succeeded at certain "meta-learning" tasks and could generalize the function of a single input-output pair. The GPT-3 release paper gave examples of [translation](https://brightworks.com.sg) and cross-linguistic transfer learning in between English and Romanian, and in between English and German. [184] +
GPT-3 significantly [enhanced benchmark](https://gitea.shoulin.net) results over GPT-2. OpenAI cautioned that such scaling-up of language models might be approaching or experiencing the fundamental capability constraints of predictive language designs. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not right away to the public for concerns of possible abuse, although OpenAI planned to enable gain access to through a paid cloud API after a two-month complimentary personal beta that started in June 2020. [170] [189] +
On September 23, 2020, GPT-3 was licensed exclusively to Microsoft. [190] [191] +
Codex
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Announced in mid-2021, Codex is a descendant of GPT-3 that has actually furthermore been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://git.sysoit.co.kr) powering the [code autocompletion](http://47.114.82.1623000) tool GitHub Copilot. [193] In August 2021, an API was launched in personal beta. [194] According to OpenAI, the model can develop working code in over a lots shows languages, a lot of efficiently in Python. [192] +
Several concerns with glitches, design flaws and security vulnerabilities were mentioned. [195] [196] +
GitHub Copilot has actually been accused of giving off copyrighted code, with no author attribution or license. [197] +
OpenAI revealed that they would cease assistance for Codex API on March 23, 2023. [198] +
GPT-4
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On March 14, 2023, OpenAI revealed the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They announced that the upgraded technology passed a [simulated law](http://39.101.134.269800) school bar examination with a rating around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might likewise check out, evaluate or generate approximately 25,000 words of text, and compose code in all major programming languages. [200] +
Observers reported that the iteration of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based iteration, [kousokuwiki.org](http://kousokuwiki.org/wiki/%E5%88%A9%E7%94%A8%E8%80%85:Addie011903410) with the caution that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has declined to expose various technical details and stats about GPT-4, such as the exact size of the model. [203] +
GPT-4o
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On May 13, 2024, OpenAI announced and launched GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained state-of-the-art lead to voice, multilingual, and vision criteria, setting new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) benchmark compared to 86.5% by GPT-4. [207] +
On July 18, 2024, OpenAI launched GPT-4o mini, a smaller sized version of GPT-4o replacing GPT-3.5 Turbo on the ChatGPT interface. Its API costs $0.15 per million input tokens and $0.60 per million output tokens, compared to $5 and $15 respectively for GPT-4o. OpenAI anticipates it to be particularly beneficial for enterprises, startups and developers looking for to automate services with [AI](https://kohentv.flixsterz.com) agents. [208] +
o1
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On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have been [developed](http://119.3.70.2075690) to take more time to think about their responses, causing higher accuracy. These designs are especially efficient in science, coding, and reasoning jobs, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:WilliamHolyfield) and were made available to ChatGPT Plus and Staff member. [209] [210] In December 2024, o1-preview was replaced by o1. [211] +
o3
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On December 20, 2024, OpenAI unveiled o3, the successor of the o1 reasoning design. OpenAI likewise revealed o3-mini, a lighter and faster variation of OpenAI o3. Since December 21, 2024, this design is not available for public use. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the opportunity to obtain early access to these models. [214] The design is called o3 instead of o2 to prevent confusion with telecoms companies O2. [215] +
Deep research study
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Deep research study is an agent developed by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 model to [perform substantial](https://www.speedrunwiki.com) web surfing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools enabled, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:LinHeilman) it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120] +
Image category
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CLIP
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Revealed in 2021, CLIP ([Contrastive Language-Image](https://git.pandaminer.com) Pre-training) is a design that is trained to evaluate the semantic resemblance between text and images. It can especially be used for image category. [217] +
Text-to-image
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DALL-E
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[Revealed](http://git.iloomo.com) in 2021, DALL-E is a Transformer design that creates images from textual descriptions. [218] DALL-E uses a 12-billion-parameter version of GPT-3 to interpret natural [language](http://47.110.52.1323000) inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of a sad capybara") and [generate](http://27.154.233.18610080) corresponding images. It can produce pictures of practical objects ("a stained-glass window with an image of a blue strawberry") along with things that do not exist in truth ("a cube with the texture of a porcupine"). Since March 2021, no API or [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:HenryUve117) code is available.
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DALL-E 2
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In April 2022, OpenAI announced DALL-E 2, an upgraded variation of the design with more sensible results. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a brand-new fundamental system for converting a text description into a 3[-dimensional](https://www.videomixplay.com) model. [220] +
DALL-E 3
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In September 2023, OpenAI revealed DALL-E 3, a more effective model better able to create images from [complicated descriptions](https://talktalky.com) without manual prompt engineering and render intricate details like hands and text. [221] It was released to the public as a ChatGPT Plus feature in October. [222] +
Text-to-video
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Sora
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Sora is a text-to-video design that can create videos based upon brief detailed triggers [223] as well as extend existing videos forwards or backwards in time. [224] It can produce videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of generated videos is unidentified.
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[Sora's advancement](https://3srecruitment.com.au) group named it after the Japanese word for "sky", to signify its "unlimited imaginative potential". [223] Sora's innovation is an adjustment of the innovation behind the DALL ยท E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos licensed for that function, but did not reveal the number or the precise sources of the videos. [223] +
OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, mentioning that it could produce videos up to one minute long. It likewise shared a technical report [highlighting](https://trackrecord.id) the approaches used to train the design, and the model's capabilities. [225] It acknowledged a few of its drawbacks, consisting of battles replicating complex physics. [226] Will Douglas Heaven of the MIT [Technology Review](https://firemuzik.com) called the presentation videos "remarkable", however noted that they must have been cherry-picked and might not represent Sora's [normal output](https://video.chops.com). [225] +
Despite uncertainty from some academic leaders following Sora's public demonstration, significant entertainment-industry figures have shown substantial interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology's ability to produce reasonable video from text descriptions, mentioning its prospective to [revolutionize storytelling](https://www.ahrs.al) and [material development](https://www.anetastaffing.com). He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to pause strategies for broadening his Atlanta-based motion picture studio. [227] +
Speech-to-text
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Whisper
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Released in 2022, Whisper is a general-purpose speech recognition design. [228] It is trained on a big dataset of varied audio and is also a multi-task design that can perform multilingual speech recognition in addition to speech translation and language identification. [229] +
Music generation
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MuseNet
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Released in 2019, MuseNet is a deep neural net trained to [predict subsequent](https://gitlab.grupolambda.info.bo) musical notes in MIDI music files. It can generate songs with 10 instruments in 15 styles. According to The Verge, a tune generated by MuseNet tends to begin fairly however then fall under chaos the longer it plays. [230] [231] In popular culture, initial applications of this tool were utilized as early as 2020 for the web psychological thriller Ben Drowned to create music for the titular character. [232] [233] +
Jukebox
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Released in 2020, Jukebox is an open-sourced algorithm to create music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a snippet of lyrics and outputs song samples. OpenAI stated the songs "reveal regional musical coherence [and] follow conventional chord patterns" but acknowledged that the tunes lack "familiar larger musical structures such as choruses that duplicate" which "there is a considerable gap" in between Jukebox and human-generated music. The Verge stated "It's technologically remarkable, even if the results seem like mushy variations of tunes that might feel familiar", while Business Insider mentioned "surprisingly, some of the resulting songs are catchy and sound genuine". [234] [235] [236] +
Interface
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Debate Game
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In 2018, OpenAI released the Debate Game, which teaches devices to discuss toy problems in front of a human judge. The function is to research whether such a technique may assist in auditing [AI](http://39.99.158.114:10080) choices and in developing explainable [AI](https://publiccharters.org). [237] [238] +
Microscope
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Released in 2020, Microscope [239] is a collection of visualizations of every substantial layer and neuron of eight neural network models which are often studied in interpretability. [240] Microscope was [produced](https://seekinternship.ng) to analyze the functions that form inside these neural networks quickly. The models included are AlexNet, VGG-19, various versions of Inception, and various variations of CLIP Resnet. [241] +
ChatGPT
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Launched in November 2022, ChatGPT is an artificial intelligence tool built on top of GPT-3 that provides a conversational interface that allows users to ask questions in natural language. The system then reacts with an answer within seconds.
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